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All Journal Journal of Creative Student Research Student Research Journal
Willy Pramudia Ananta
Universitas Negeri Medan
Religion Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences Transportation

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Klasifikasi Tumor Otak Menggunakan Local Binary Pattern dan SVM Classifier Wahyu Ardiantito S; Stacyana Jesika Surianto; Suci Ramadhani; Willy Pramudia Ananta
Student Research Journal Vol. 1 No. 6 (2023): Desember : Student Research Journal
Publisher : Sekolah Tinggi Ilmu Administrasi (STIA) Yappi Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/srjyappi.v1i6.823

Abstract

Brain tumors are abnormal cell growths in brain tissue that can be life-threatening. This study aims to classify brain tumors to help early diagnosis. The method used is to extract features from brain MRI images using Local Binary Pattern (LBP) and then classified with Support Vector Machine (SVM). The data used were 2044 brain MRI images consisting of 3 classes namely meningioma, no tumor, and pituitary. The best results were obtained using LBP with a radius of 1 and the number of neighbors 8, while the best SVM model used the RBF kernel with a C value of 50, resulting in 88% accuracy, 86% precision, and 87% recall. It can be concluded that the combination of LBP and SVM methods is effective enough to classify brain tumor types to support early diagnosis.
Analisis Prediksi Harga Rumah di Bandung Menggunakan Regresi Linear Berganda Rafif Nauval Tuah Siregar; Vijay Sitorus; Willy Pramudia Ananta
Journal of Creative Student Research Vol. 1 No. 6 (2023): Desember : Journal of Creative Student Research
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jcsrpolitama.v1i6.3038

Abstract

This research aims to develop a model for estimating house prices in the Bandung area using the Multiple Linear Regression approach. House prices play a significant role in the decision-making process for purchases. The ability of this model to predict house prices with high accuracy provides significant benefits for potential buyers, sellers, and various stakeholders in the housing industry. Data on house prices and potential variables such as land area, building area, number of bedrooms, nearby facilities, and geographical location were collected for analysis. The use of Multiple Linear Regression allows for a deeper understanding of the relationships between these variables and the value of the house. The analysis results show a strong correlation between these variables and house prices in Bandung. The developed Multiple Linear Regression model can provide satisfactory predictions of house prices. This model can be used as a tool for both homebuyers and sellers to determine fair prices and assist property developers in identifying key factors influencing house prices in the Bandung region.
Co-Authors Rafif Nauval Tuah Siregar Stacyana Jesika Surianto Suci Ramadhani Vijay Sitorus Wahyu Ardiantito S